The schedule below is tentative and will be updated (frequently) as we progress through the quarter.
In the table below, VMLS refers to the ENGR104 textbook, Introduction to Applied Linear Algebra - Vectors, Matrices, and Least Squares.
| Date | Slides | Additional reading |
| 4/2 | course information, overview and examples | VMLS, chapters 12 and section 13.1. |
| 4/4 | predictors | VMLS, section 13.1. |
| 4/9 | predictors, validation | VMLS, section 13.2. |
| 4/11 | validation, features | VMLS, section 13.3. |
| 4/16 | features | VMLS, section 13.3. |
| 4/18 | empirical risk minimization and house prices example | VMLS, section 15.4. |
| 4/23 | constant predictors, non-quadratic losses | |
| 4/25 | non-quadratic regularizers | |
| 4/30 | optimization | |
| 5/2 | prox-gradient method | |
| 5/7 | boolean classification | VMLS, chapter 14. |
| 5/9 | multi-class classification | VMLS, section 14.3. |
| 5/14 | multi-class classification | VMLS, section 14.3. |
| 5/16 | neural networks | |
| 5/21 | neural networks | |
| 5/23 | unsupervised learning | |
| 5/28 | principal component analysis | |
| 5/30 | principal component analysis | |
| 6/4 | ||